Our focus is on using a robust CV algorithm to provide cognitive availability input to the controller rather than evaluating the various CV algorithms. The Deepgaze algorithm [1]} is used in our system for head pose estimation. It makes use of state-of-the-art methods such as a pre-trained Convolutional Neural Network (CNN), is open source and actively maintained. In our implementation, deepgaze measures the operator's head rotation on the vertical axis (yaw). The head rotation was filtered through an exponential moving average (EMA) [2]} in order to reduce noise in the measurements. The parameters of the EMA were calculated by analyzing data regarding the attention of the operator in the secondary task in previous experiments. Lastly, baseline data on what values of rotation constitute attending or not at the GUI were gathered in a pilot experiment and in a standardized way. These were mapped into the fuzzy input for cognitive availability.
